QGIS Integration Guide

Learn how to visualize and analyze enriched LiDAR files in QGIS with geometric features for building component analysis.

Overview

Enriched LAZ files from this library are fully compatible with QGIS and include 30+ geometric features perfect for:

• Building component visualization
• Surface analysis (walls, roofs, ground)
• Edge detection and linearity analysis
• Density and roughness mapping

Prerequisites

QGIS Requirements

• QGIS 3.10+ with point cloud support
• LAZ/LAS reader plugin (usually included)

Verify Installation

# Check if QGIS can read point clouds
# Open QGIS and look for: Layer > Add Layer > Add Point Cloud Layer

Step 1: Enrich LAZ Files

First, create enriched LAZ files with geometric features:

# Enrich tiles with all features
ign-lidar-hd enrich \
  --input-dir /path/to/raw_tiles/ \
  --output /path/to/enriched_tiles/ \
  --mode full \
  --num-workers 4

This adds 30+ attributes to each point including:

• Surface properties: planarity, sphericity, linearity
• Geometric features: normals, curvature, roughness
• Building analysis: verticality, wall_score, roof_score
• Height metrics: height_above_ground, vertical_std

Step 2: Load in QGIS

Import Point Cloud

1. Open QGIS
2. Menu: Layer → Add Layer → Add Point Cloud Layer
3. Browse: Select your enriched LAZ file
4. Click Add: Point cloud appears in the map

Initial Display

The point cloud will initially display with default elevation coloring. You'll see your LiDAR data as colored points.

Step 3: Visualize Features

Access Symbology

1. Right-click on the layer → Properties
2. Symbology tab
3. Rendered by: Attribute
4. Attribute: Select feature to visualize

Available Features

Core Geometric Features

Feature	Description	Range	Use Case
planarity	Surface flatness score	0-1	Detect walls, roofs, ground
linearity	Edge/line score	0-1	Find building edges, cables
sphericity	3D spherical score	0-1	Identify vegetation, rounded objects
curvature	Surface curvature	varies	Edge detection, surface analysis
roughness	Surface texture	varies	Material classification

Building-Specific Features

Feature	Description	Range	Use Case
verticality	Vertical orientation score	0-1	Wall detection
wall_score	Wall probability	0-1	Building facade analysis
roof_score	Roof probability	0-1	Roof surface detection
height_above_ground	Normalized height	meters	Multi-story analysis

Normal Vectors

Feature	Description	Range	Use Case
normal_x	X component of surface normal	-1 to 1	Surface orientation
normal_y	Y component of surface normal	-1 to 1	Surface orientation
normal_z	Z component of surface normal	-1 to 1	Surface orientation

Step 4: Recommended Visualizations

Building Wall Detection

Goal: Highlight building walls

Settings:

• Attribute: wall_score
• Color Ramp: RdYlGn (Red-Yellow-Green)
• Min: 0.0, Max: 1.0
• Filter: wall_score > 0.7 (optional)

Interpretation:

• Red (0.0-0.3): Non-wall points (ground, vegetation)
• Yellow (0.4-0.6): Possible walls
• Green (0.7-1.0): High confidence walls

Roof Surface Analysis

Goal: Identify flat roof surfaces

Settings:

• Attribute: planarity
• Color Ramp: Viridis
• Min: 0.0, Max: 1.0
• Filter: roof_score > 0.5 AND verticality < 0.3

Interpretation:

• Dark blue (0.0-0.3): Irregular surfaces
• Green-yellow (0.7-1.0): Flat surfaces (roof candidates)

Building Edge Detection

Goal: Find building corners and edges

Settings:

• Attribute: linearity
• Color Ramp: Hot (Black to White)
• Min: 0.0, Max: 1.0
• Filter: linearity > 0.6

Interpretation:

• Black (0.0-0.3): Flat surfaces
• White (0.7-1.0): Linear features (edges, corners)

Height-based Analysis

Goal: Visualize building stories

Settings:

• Attribute: height_above_ground
• Color Ramp: Turbo or Plasma
• Min: 0, Max: 30 (adjust to your data)
• Filter: wall_score > 0.5

Interpretation:

• Blue (0-3m): Ground floor
• Green (3-6m): First floor
• Yellow-Red (6m+): Upper floors

Advanced Filtering

Multi-attribute Filtering

Use QGIS expressions for complex filtering:

-- High-confidence building walls above ground floor
"wall_score" > 0.8 AND "height_above_ground" > 3 AND "height_above_ground" < 20

-- Flat roof surfaces
"planarity" > 0.7 AND "roof_score" > 0.6 AND "verticality" < 0.2

-- Building edges at upper levels
"linearity" > 0.6 AND "height_above_ground" > 3

-- Vegetation points (high sphericity, low planarity)
"sphericity" > 0.5 AND "planarity" < 0.4

Expression Builder

1. Right-click layer → Filter
2. Expression builder opens
3. Enter expression using attribute names
4. Test and Apply

Visualization Examples

Example 1: Building Component Classification

Steps:

1. Load enriched LAZ file
2. Create 3 separate layers (duplicate the layer)
3. Layer 1: Walls (wall_score > 0.7, red color)
4. Layer 2: Roofs (roof_score > 0.7 AND verticality < 0.3, blue color)
5. Layer 3: Ground (height_above_ground < 1, green color)

Result: Color-coded building components

Example 2: Surface Quality Analysis

Steps:

1. Visualize by roughness attribute
2. Use discrete classes: Smooth (0-0.1), Medium (0.1-0.3), Rough (0.3+)
3. Apply to identify material types

Example 3: Architectural Detail Detection

Steps:

1. Filter: linearity > 0.6 AND height_above_ground > 2
2. Color by height_above_ground
3. Result: Building edges and architectural details colored by height

Troubleshooting

Issue: LAZ File Won't Open

Symptoms: "Cannot read file" error

Solutions:

1. Check QGIS version (needs 3.10+)
2. Verify LAZ support: Plugins → Manage Plugins → Search "LAZ"
3. Try converting to LAS format if needed

Issue: No Custom Attributes Visible

Symptoms: Only X, Y, Z, Intensity available

Verification:

# Check if enrichment worked
python -c "
import laspy
las = laspy.read('enriched_file.laz')
print('Extra dimensions:', las.point_format.extra_dimension_names)
"

Solutions:

1. Re-run enrichment process
2. Ensure enrichment completed successfully
3. Check file wasn't overwritten

Issue: Visualization Looks Noisy

Symptoms: Point cloud shows scan line artifacts

Cause: Using old fixed k-neighbors instead of adaptive radius

Solution: Update to latest version with radius-based feature computation

Issue: Performance Problems

Symptoms: QGIS slow with large files

Solutions:

1. Limit point display: Set maximum points in layer properties
2. Use Level of Detail: Enable in rendering settings
3. Clip to area: Use spatial filter for region of interest
4. Simplify: Process smaller tiles or downsample

Data Export

Export Filtered Points

1. Right-click layer → Export → Save Features As
2. Format: LAS/LAZ
3. Filter: Use current filter or create new one
4. Save: Filtered point cloud as new file

Export to Other Formats

• CSV: For spreadsheet analysis
• Shapefile: For vector GIS analysis (centroids)
• GeoJSON: For web mapping

Performance Tips

Large Dataset Handling

# Process smaller tiles for better QGIS performance
ign-lidar-hd process \
  --input large_tile.laz \
  --output patches/ \
  --patch-size 20.0  # Smaller patches

# Or clip large files geographically
# Use QGIS Clip tool or command line utilities

Display Optimization

• Point budget: Limit to 1M points for smooth interaction
• Level of detail: Enable automatic point reduction
• Attribute caching: QGIS will cache attribute statistics

Integration with Other Tools

Combine with Vector Data

• Building footprints: Overlay building polygons
• Road networks: Add context with street data
• Property boundaries: Combine with cadastral data

Export Results

• Screenshots: High-resolution map exports
• Analysis results: Statistical summaries
• Filtered datasets: Export classified points

See Also

• Basic Usage Guide - Creating enriched LAZ files
• CLI Commands - Enrichment command reference
• QGIS Troubleshooting - Common issues and solutions
• Memory Optimization - Performance tuning